Asian/Asian-American Learners in North America - Revision history

Ryan: Added Jeong et al (2022)

2022-08-04T20:03:24Z

Added Jeong et al (2022)

← Older revision		Revision as of 16:03, 4 August 2022
Line 20:		Line 20:
	* Roughly equal accuracy across racial groups		* Roughly equal accuracy across racial groups
	* Slightly better accuracy (~1%) across racial groups when including race in model		* Slightly better accuracy (~1%) across racial groups when including race in model


			Jeong et al. (2022) [https://fated2022.github.io/assets/pdf/FATED-2022_paper_Jeong_Racial_Bias_ML_Algs.pdf]
			* Predicting 9th grade math score from academic performance, surveys, and demographic information
			* Despite comparable accuracy, model tends to overpredict Asian students' performance
			* Several fairness correction methods equalize false positive and false negative rates across groups.

Ryan: re-ordering

2022-06-10T10:03:21Z

re-ordering

← Older revision		Revision as of 06:03, 10 June 2022
Line 1:		Line 1:
	Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf]		Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf]
	* Models predicting student's high school dropout		* Models predicting student's high school dropout
	* The decision trees showed little difference in AUC among White, Black, Hispanic~~, Asian~~, American Indian and Alaska Native, and Native Hawaiian and Pacific Islander.		* The decision trees showed little difference in AUC among Asian, White, Black, Hispanic, American Indian and Alaska Native, and Native Hawaiian and Pacific Islander.


Line 12:		Line 12:
	Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf]		Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf]
	* Models predicting college success (or median grade or above)		* Models predicting college success (or median grade or above)
	* Random forest algorithms performed significantly ~~worse~~ for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural~~) than non-URM students (White and Asian~~)		* Random forest algorithms performed significantly better for non-URM students (Asian and White) than for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural)
	* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values		* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values

Ryan at 13:02, 21 May 2022

2022-05-21T13:02:26Z

← Older revision		Revision as of 09:02, 21 May 2022
Line 14:		Line 14:
	* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)		* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)
	* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values		* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values


	Jiang & Pardos (2021) [https://dl.acm.org/doi/pdf/10.1145/3461702.3462623 pdf]		Jiang & Pardos (2021) [https://dl.acm.org/doi/pdf/10.1145/3461702.3462623 pdf]

Ryan: Added Jiang & Pardos

2022-05-21T13:02:15Z

Added Jiang & Pardos

← Older revision		Revision as of 09:02, 21 May 2022
Line 14:		Line 14:
	* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)		* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)
	* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values		* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values

			Jiang & Pardos (2021) [https://dl.acm.org/doi/pdf/10.1145/3461702.3462623 pdf]
			* Predicting university course grades using LSTM
			* Roughly equal accuracy across racial groups
			* Slightly better accuracy (~1%) across racial groups when including race in model

Ryan: clarification

2022-05-19T13:48:25Z

clarification

← Older revision		Revision as of 09:48, 19 May 2022
Line 6:		Line 6:
	Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring page]		Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring page]
	* Automated scoring models for evaluating English essays, or e-rater		* Automated scoring models for evaluating English essays, or e-rater
	* E-Rater gave significantly better scores than human rater for 11th grade essays written by Hispanic students and Asian-American ~~students than White~~ students		* E-Rater gave significantly better scores than human rater for 11th grade essays written by Hispanic students and Asian-American students

Seiyon at 12:29, 19 May 2022

2022-05-19T12:29:09Z

← Older revision		Revision as of 08:29, 19 May 2022
Line 6:		Line 6:
	Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring page]		Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring page]
	* Automated scoring models for evaluating English essays, or e-rater		* Automated scoring models for evaluating English essays, or e-rater
	* E-Rater gave significantly better scores for 11th grade essays written by Hispanic students and Asian-American students than White students		* E-Rater gave significantly better scores than human rater for 11th grade essays written by Hispanic students and Asian-American students than White students

Seiyon at 11:36, 19 May 2022

2022-05-19T11:36:26Z

← Older revision		Revision as of 07:36, 19 May 2022
Line 4:		Line 4:


	Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring ~~pdf~~]		Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring page]
	* Automated scoring models for evaluating English essays, or e-rater		* Automated scoring models for evaluating English essays, or e-rater
	* E-Rater gave significantly better scores for 11th grade essays written by Hispanic students and Asian-American students than White students		* E-Rater gave significantly better scores for 11th grade essays written by Hispanic students and Asian-American students than White students

Seiyon at 20:20, 18 May 2022

2022-05-18T20:20:33Z

← Older revision		Revision as of 16:20, 18 May 2022
Line 6:		Line 6:
	Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring pdf]		Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring pdf]
	* Automated scoring models for evaluating English essays, or e-rater		* Automated scoring models for evaluating English essays, or e-rater
	* E-~~rater~~ gave significantly ~~higher score~~ for 11th grade essays written by ~~Asian American and Hispanic students, particularly,~~ Hispanic ~~female~~ students		* E-Rater gave significantly better scores for 11th grade essays written by Hispanic students and Asian-American students than White students
	* The score difference between human rater and e-~~rater was significantly smaller for 11th grade essays written by~~ White ~~and African American~~ students
	* E-rater gave slightly lower score for GRE essays (argument and issue) written by Black test-takers while e-rated scores were higher for Asian test-takers in the U.S

Seiyon at 11:46, 18 May 2022

2022-05-18T11:46:36Z

← Older revision		Revision as of 07:46, 18 May 2022
Line 11:		Line 11:


	Lee and Kizilcec (2020) [[https://arxiv.org/pdf/2007.00088.pdf~~](https://arxiv.org/pdf/2007.00088.pdf)~~ pdf]		Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf]
	* Models predicting college success (or median grade or above)		* Models predicting college success (or median grade or above)
	* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)		* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)
	* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values		* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values

Seiyon at 11:46, 18 May 2022

2022-05-18T11:46:09Z

← Older revision		Revision as of 07:46, 18 May 2022
Line 9:		Line 9:
	* The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students		* The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students
	* E-rater gave slightly lower score for GRE essays (argument and issue) written by Black test-takers while e-rated scores were higher for Asian test-takers in the U.S		* E-rater gave slightly lower score for GRE essays (argument and issue) written by Black test-takers while e-rated scores were higher for Asian test-takers in the U.S


			Lee and Kizilcec (2020) [[https://arxiv.org/pdf/2007.00088.pdf](https://arxiv.org/pdf/2007.00088.pdf) pdf]
			* Models predicting college success (or median grade or above)
			* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)
			* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values